228 citations · 358 across the 5 of their papers we have counts for
6 papers
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Kate Rakelly, Abhishek Gupta, Carlos Florensa +1
Mutual information maximization provides an appealing formalism for learning representations of data. In the context of reinforcement learning (RL), such representations can accele…
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly +2
Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training ta…
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen +2
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable a…
Few-Shot Segmentation Propagation with Guided Networks
Kate Rakelly, Evan Shelhamer, Trevor Darrell +2
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of…
Clockwork Convnets for Video Semantic Segmentation
Evan Shelhamer, Kate Rakelly, Judy Hoffman +1
Recent years have seen tremendous progress in still-image segmentation; however the naïve application of these state-of-the-art algorithms to every video frame requires considerabl…
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs +4
Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-i…